| --- |
| license: mit |
| library_name: mlstac |
| tags: |
| - earth-observation |
| - remote-sensing |
| - cloud-segmentation |
| - chris-proba |
| - semantic-segmentation |
| --- |
| |
| # CHRIS-PROBA1 — Cloud and shadow segmentation |
|
|
| Cloud and cloud-shadow segmentation for **CHRIS/PROBA-1** imagery. The model is a |
| two-network ensemble (RegNetY-004 + ConvNeXtV2-nano, U-Net heads) finetuned on RGBN |
| bands and unified so the same weights handle both DN and TOA inputs. |
|
|
| [](https://colab.research.google.com/drive/1ydANmqaFExOGghxXqupnOQvbro7RFsd7?usp=sharing) |
|
|
| ## Install |
|
|
| ```bash |
| pip install mlstac |
| # runtime dependencies for this model: |
| pip install torch segmentation-models-pytorch pytorch-lightning timm rasterio numpy |
| ``` |
|
|
| ## Usage |
|
|
| ```python |
| import mlstac |
| |
| # 1. Load the metadata and download the model files |
| model = mlstac.load( |
| "https://huggingface.co/isp-uv-es/CHRIS-PROBA1/resolve/main/mlm.json" |
| ) |
| local = model.download("CHRIS-PROBA1") |
| |
| # 2. Build the ensemble (loads both checkpoints) |
| net = local.compiled_model(device="cuda") |
| |
| # 3a. Segment a raw CHRIS GeoTIFF end to end. |
| # mode_n is the CHRIS acquisition mode; source is 'dn' or 'toa' |
| # (or None to guess it from the file name). |
| mask = local.module.predict_chris( |
| "image_mode_1/scene_DN.tif", model=net, mode_n=1, source="dn" |
| ) |
| |
| # 3b. Mode 6 is CHRIS mode 20: 4 raw bands, DN only (no TOA). |
| mask20 = local.module.predict_chris( |
| "image_mode_20/scene_DN.tif", model=net, mode_n=6, source="dn" |
| ) |
| ``` |
|
|
| If you already have a 4-band RGBN array `(4, H, W)`, you can skip the CHRIS |
| preprocessing and call the model directly: |
|
|
| ```python |
| mask = local.module.predict_large(rgbn_array, model=net) |
| ``` |
|
|
| ## Output classes |
|
|
| | Value | Class | |
| |-------|-------------| |
| | 0 | clear | |
| | 1 | thick cloud | |
| | 2 | thin cloud | |
| | 3 | shadow | |
| | 99 | nodata | |
|
|
| ## Supported CHRIS modes |
|
|
| The loader builds the RGBN stack (Red, Green, Blue, NIR) from the raw cube |
| according to the acquisition mode. Modes 1-5 average several bands per channel |
| and exist in both DN and TOA. Mode 6 is CHRIS mode 20: it has 4 bands used |
| directly (no averaging) and DN only. |
|
|
| | `mode_n` | CHRIS mode | DN | TOA | |
| |----------|-----------|----|-----| |
| | 1 | 1 | ✓ | ✓ | |
| | 2 | 2 | ✓ | ✓ | |
| | 3 | 3 | ✓ | ✓ | |
| | 4 | 4 | ✓ | ✓ | |
| | 5 | 5 | ✓ | ✓ | |
| | 6 | 20 | ✓ | — | |
|
|
| DN and TOA use different radiometric scales before a fixed clip, so passing the |
| correct `source` matters. Pass `source='dn'` or `source='toa'`, or leave it as |
| `None` to infer it from the file name. |
|
|
| ## Example scenes |
|
|
| The `examples/` folder holds one paired scene per mode (`image_mode_1` ... |
| `image_mode_5` with DN and TOA, `image_mode_20` with DN only) to try the model. |
|
|
| ## Citation |
|
|
| If you use this model, please cite the CHRIS/PROBA-1 cloud segmentation work from |
| the Image and Signal Processing (ISP) group, Universitat de València. |
|
|
| ## License |
|
|
| MIT |
